Date of Award
Summer 9-1-2025
Document Type
Thesis
Degree Name
Master of Science (M.S.)
Department
Computer and Information Sciences
Language
English
First Advisor
DR. PANNEER SELVAM SANTHALINGAM
Abstract
For accurately estimating the depth of environments with varying lighting conditions, reliable methods are limited. By utilizing wireless sensor technology in conjunction with cameras, a wide range of environments can be visualized, and objects within these environments can be tracked and monitored. Such methods offer cost-effective alternatives and provide a more secure, data-at-rest option for individuals with low vision, while also enhancing machine perception. In this work, we develop such a prototype that utilizes wireless sensors and cameras, which act in sync, enabling us to estimate the depth of objects within varying lighting environments to a level that is recognizable to the human eye. This approach yields more accurate results than a camera or radar alone. The resulting dataset, along with methods to align data obtained through multiple sensors, methods to estimate depth, and methods to address sparse and noisy signal measurements from wireless sensors, serves as a benchmark for studying the fundamental research challenges in multi-sensor solutions.
Recommended Citation
Devero-Belfon, Alston D., "Multi-Modal Depth Estimation Using Camera and MMWAVE Sensors" (2025). CUNY Academic Works.
https://academicworks.cuny.edu/bc_etds/33
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Disability Studies Commons, Other Electrical and Computer Engineering Commons, Signal Processing Commons
